ML Login Source Classification for Account Security
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Solution Overview
Problem
Existing online authentication systems face challenges in distinguishing between authorized and unauthorized login attempts, particularly when valid credentials are reused or compromised, making it difficult to determine appropriate access levels for user accounts.
Innovation Solution
A system that uses a trained machine learning model to analyze user activity observations from login attempts, applying a clustering algorithm to determine the source of the attempt and assign an authentication level, allowing for restricted or full access based on classification into authorized, unauthorized, or authorized third-party device clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication systems use valid credentials to grant access, then authorized users can access their accounts, but unauthorized users with stolen or leaked credentials can also access accounts
Solution Approach 1:
The patent segments the authentication process into multiple independent analysis dimensions: device characteristics, network environment, behavioral patterns, and temporal factors. Each dimension is evaluated separately by the machine learning model to determine the overall authentication risk, preventing single-point failures and improving reliability.
Solution Approach 2:
The patent introduces an intermediary machine learning classification system between the credential verification and account access. This intermediary analyzes multiple factors beyond simple credential matching to determine whether to grant access, effectively blocking unauthorized users while allowing legitimate users to access their accounts.
2Object-affected harmful factors
If the system implements strict access control to block unauthorized attempts, then account security is improved, but legitimate third-party access (such as financial aggregators) is restricted
Solution Approach 1:
The patent dynamically changes authentication parameters based on the analyzed login attempt characteristics. For legitimate third-party applications with established behavioral patterns and trusted device profiles, the system adjusts authentication requirements to allow access. For suspicious attempts, stricter parameters are applied, enabling flexible adaptation to different access scenarios.
Solution Approach 2:
The authentication system transitions from static credential-based access control to dynamic risk-based authentication. The machine learning model continuously evaluates login attempts and adjusts access decisions in real-time based on changing conditions, device states, and behavioral patterns, allowing legitimate third-party access while blocking unauthorized attempts.
3Measurement precision
If the system analyzes multiple factors to determine login source, then authentication accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal machine learning classification system that handles multiple authentication analysis functions within a single model. The system simultaneously evaluates device characteristics, network environment, behavioral patterns, and temporal factors, reducing overall system complexity despite the multifaceted analysis required for accurate login source identification.
Data Source
AI summary
A source of a login attempt to a user account can be classified using machine learning. For example, a computing system can input user activity observations associated with one or more login attempts to one or more user accounts into a trained machine learning model. One or more distinguishing factors for the one or more login attempts can be received from the trained machine learning model. The computing system can determine a source of a current login attempt by applying a clustering algorithm to current values of the one or more distinguishing factors. The current values may be derived from current user activity observations associated with the current login attempt. The computing system can determine an authentication level for the current login attempt to the user account based on the source of the current login attempt.